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        data-chapter-title="拓展：高级绘图"
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                            <b>1.</b>
                        
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                        Python数据课程 软件和环境安装
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                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                        pip和Virtualenv
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                        标记语言：Markdown
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                        Markdown语法
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                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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                            <b>4.1.</b>
                        
                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                            <b>4.3.</b>
                        
                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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                            <b>5.</b>
                        
                        Python可视化
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
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                            <b>5.3.</b>
                        
                        提升：绘图组件
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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                            <b>5.7.</b>
                        
                        拓展：pylab
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                            <b>6.</b>
                        
                        数据分析必备知识点
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                            <b>7.</b>
                        
                        数据分析流程
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<h1 id="matplotlib&#x9AD8;&#x7EA7;&#x7ED8;&#x56FE;">Matplotlib-&#x9AD8;&#x7EA7;&#x7ED8;&#x56FE;</h1>
<pre><code class="lang-python">%matplotlib notebook
</code></pre>
<hr>
<h1 id="&#x4E09;&#x7EF4;&#x56FE;&#x50CF;">&#x4E09;&#x7EF4;&#x56FE;&#x50CF;</h1>
<p>&#x4E09;&#x7EF4;&#x56FE;&#x50CF;&#x4E3B;&#x8981;&#x7528;&#x4E8E;&#x4E09;&#x7EF4;&#x6570;&#x636E;&#x53EF;&#x89C6;&#x5316;</p>
<pre><code class="lang-python">
<span class="hljs-keyword">from</span> mpl_toolkits.mplot3d <span class="hljs-keyword">import</span> Axes3D  <span class="hljs-comment"># &#x8F7D;&#x5165;&#x4E09;&#x7EF4;&#x5B50;&#x5E93;</span>
</code></pre>
<pre><code class="lang-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

<span class="hljs-comment"># plt.style.use(&apos;seaborn&apos;)</span>
plt.rcParams[<span class="hljs-string">&apos;font.family&apos;</span>] = [<span class="hljs-string">&apos;Arial Unicode MS&apos;</span>, <span class="hljs-string">&apos;Microsoft Yahei&apos;</span>, <span class="hljs-string">&apos;SimHei&apos;</span>, <span class="hljs-string">&apos;sans-serif&apos;</span>]
</code></pre>
<pre><code class="lang-python">np.random.randint(<span class="hljs-number">1</span>,<span class="hljs-number">100</span>,<span class="hljs-number">20</span>)
</code></pre>
<pre><code>array([55, 40, 56, 28, 85, 75, 88, 14, 84, 76, 82, 72, 11, 68, 29, 99, 20,
       74, 38, 90])
</code></pre><pre><code class="lang-python"><span class="hljs-comment"># &#x521B;&#x5EFA;&#x7236;&#x5BF9;&#x8C61;</span>
fig = plt.figure()
<span class="hljs-comment"># &#x521B;&#x5EFA;&#x5B50;&#x5BF9;&#x8C61;&#xFF08;&#x4E09;&#x7EF4;&#xFF09;</span>
ax = Axes3D(fig)

<span class="hljs-comment"># &#x5404;&#x8F74;&#x751F;&#x6210;0-100&#x7684;&#x968F;&#x673A;&#x5750;&#x6807;&#x6570;&#x636E;20&#x4E2A;</span>
x = np.random.randint(<span class="hljs-number">0</span>, <span class="hljs-number">100</span>, <span class="hljs-number">20</span>)
y = np.random.randint(<span class="hljs-number">0</span>, <span class="hljs-number">100</span>, <span class="hljs-number">20</span>)
z = np.random.randint(<span class="hljs-number">0</span>, <span class="hljs-number">100</span>, <span class="hljs-number">20</span>)

<span class="hljs-comment"># &#x7ED8;&#x5236;&#x6563;&#x70B9;&#x56FE;</span>
<span class="hljs-comment">#ax.scatter(x, y, z)</span>

<span class="hljs-comment"># &#x7ED8;&#x5236;&#x6298;&#x7EBF;&#x56FE;</span>
ax.plot(x, y, z)

<span class="hljs-comment"># &#x5750;&#x6807;&#x8F74;&#x540D;&#x79F0;</span>
ax.set_xlabel(<span class="hljs-string">&apos;x&#x8F74;&apos;</span>)
ax.set_ylabel(<span class="hljs-string">&apos;y&#x8F74;&apos;</span>)
ax.set_zlabel(<span class="hljs-string">&apos;z&#x8F74;&apos;</span>)
ax.set_title(<span class="hljs-string">&apos;&#x6211;&#x7684;&#x4E09;&#x7EF4;&#x7ACB;&#x4F53;&#x56FE;&apos;</span>)
<span class="hljs-comment">#plt.show()</span>
</code></pre>
<pre><code>&lt;IPython.core.display.Javascript object&gt;
</code></pre><p><img src="" width="638.888905813665"></p>
<pre><code>Text(0.5,0.92,&apos;&#x6211;&#x7684;&#x4E09;&#x7EF4;&#x7ACB;&#x4F53;&#x56FE;&apos;)
</code></pre><hr>
<h1 id="&#x52A8;&#x753B;">&#x52A8;&#x753B;</h1>
<pre><code class="lang-python"><span class="hljs-keyword">from</span> matplotlib.animation <span class="hljs-keyword">import</span> FuncAnimation  <span class="hljs-comment"># &#x8F7D;&#x5165;&#x52A8;&#x753B;&#x5B50;&#x5E93;</span>
<span class="hljs-keyword">import</span> random
</code></pre>
<pre><code class="lang-python">fig, ax = plt.subplots()

line = []
<span class="hljs-comment"># &#x52A8;&#x753B;&#x6BCF;&#x5E27;&#x8C03;&#x7528;&#x7684;&#x51FD;&#x6570;</span>
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">update</span><span class="hljs-params">(i)</span>:</span>
    plt.title(i)  <span class="hljs-comment"># frame&#x7684;&#x503C;&#xFF0C;&#x968F;&#x8C03;&#x7528;&#x904D;&#x5386;</span>

        <span class="hljs-comment"># &#x7ED8;&#x5236;&#x6563;&#x70B9;&#x56FE;</span>
    s1 = random.randint(-<span class="hljs-number">10</span>, <span class="hljs-number">10</span>)  <span class="hljs-comment"># x&#x8F74;&#x5750;&#x6807;&#x6570;&#x636E;</span>
    s2 = random.randint(-<span class="hljs-number">10</span>, <span class="hljs-number">10</span>)  <span class="hljs-comment"># y&#x8F74;&#x5750;&#x6807;&#x6570;&#x636E;</span>
    <span class="hljs-comment">#ax.scatter(s1, s2)  # &#x7ED8;&#x56FE;</span>
<span class="hljs-comment">#     x = 5,</span>
<span class="hljs-comment">#     y = 4,</span>
<span class="hljs-comment">#     plt.scatter(s1,s2)</span>

    y2 = random.randint(-<span class="hljs-number">1</span>,<span class="hljs-number">1</span>)
    line.append(y2)
    plt.plot(np.array(line).cumsum(),color=<span class="hljs-string">&apos;red&apos;</span>)  <span class="hljs-comment">#&#x751F;&#x6210;&#x6570;&#x7EC4;&#xFF0C;&#x7D2F;&#x52A0;&#x5386;&#x53F2;&#x4EF7;&#x683C;</span>

<span class="hljs-comment"># &#x52A8;&#x753B;&#x65B9;&#x6CD5;</span>
ani = FuncAnimation(
    fig,  <span class="hljs-comment"># &#x52A8;&#x753B;&#x5E94;&#x7528;&#x7684;&#x56FE;&#x50CF;</span>
    update,  <span class="hljs-comment"># &#x52A8;&#x753B;&#x6BCF;&#x5E27;&#x90FD;&#x8981;&#x8C03;&#x7528;&#x7684;&#x51FD;&#x6570;</span>
    interval = <span class="hljs-number">100</span>,  <span class="hljs-comment"># &#x52A8;&#x753B;&#x5E27;&#x95F4;&#x9694;&#xFF0C;&#x6BEB;&#x79D2;</span>
    frames = <span class="hljs-number">50</span>, <span class="hljs-comment"># &#x52A8;&#x753B;&#x5E27;&#x6570;&#xFF0C;&#x5BF9;GIF&#x6709;&#x6548;&#xFF0C;&#x503C;&#x8C03;&#x7528;&#x51FD;&#x6570;&#x5185;&#x7684;i&#x53EF;&#x4EE5;&#x83B7;&#x53D6;&#x3002;&#x53EF;&#x81EA;&#x5B9A;&#x4E49;&#x503C;&#x5982;range(50,100)</span>
)
<span class="hljs-comment">#ani.save(&apos;line.html&apos;, dpi=72)</span>
plt.show()
</code></pre>
<pre><code>&lt;IPython.core.display.Javascript object&gt;
</code></pre><p><img src="" width="638.888905813665"></p>
<pre><code class="lang-python">

</code></pre>

                    
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